iCACD: an intelligent deep learning model to categorise current affairs news article for efficient journalistic process

Sachin Kumar (), Shivam Panwar (), Jagvinder Singh (), Anuj Kumar Sharma () and Zairu Nisha ()
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Sachin Kumar: University of Delhi
Shivam Panwar: University of Delhi
Jagvinder Singh: Delhi Technological University
Anuj Kumar Sharma: University of Delhi
Zairu Nisha: University of Delhi

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 5, No 34, 2572-2582

Abstract: Abstract In the present-day technology-driven world, information reaching at the individual’s doorstep sometimes becomes complex, haphazard and difficult to classify to get the insights. The endpoint consumer of the information requires processed information which is contextually suited to their needs, interests and is properly formatted and categorised. Interests and need-based categorization of news and stories would enable the user beforehand to further evaluate information deeply. For instances, the type current affairs related issues and news to read or not to read. This research work proposes an advanced current affairs classification model based on deep learning approaches called Intelligent Current Affairs Classification Using Deep Learning (iCACD). The proposed model is better than already proposed models based on machine learning approached which have been compared on accuracy and performance criteria. The proposed model is better in the following ways. Firstly, It is based on advanced deep neural network architecture. Secondly, the model advances the work to include both headline and body of the information/news articles rather than only processing headlines. Thirdly, A detailed comparative analysis and discussion on accuracy and performance with other machine leaning models have also been presented.

Keywords: Current affairs; News articles; Machine learning; Deep learning; Accuracy; Efficiency; Text classification (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13198-022-01666-6

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